{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.2实现神经网络实例</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2.2准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "# 导入 pytorch 内置的 mnist 数据\n",
    "from torchvision.datasets import mnist \n",
    "#import torchvision\n",
    "#导入预处理模块\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader\n",
    "#导入nn及优化器\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch import nn\n",
    "\n",
    "#from tensorboardX import SummaryWriter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 定义一些超参数\n",
    "train_batch_size = 64\n",
    "test_batch_size = 128\n",
    "learning_rate = 0.01\n",
    "num_epoches = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize([0.5], [0.5])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "0it [00:00, ?it/s]Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../../data/Handwritten Num Recognition\\MNIST\\raw\\train-images-idx3-ubyte.gz\n9920512it [00:17, 558613.75it/s]\nExtracting ../../data/Handwritten Num Recognition\\MNIST\\raw\\train-images-idx3-ubyte.gz to ../../data/Handwritten Num Recognition\\MNIST\\raw\n0it [00:00, ?it/s]Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../../data/Handwritten Num Recognition\\MNIST\\raw\\train-labels-idx1-ubyte.gz\n32768it [00:02, 14165.57it/s]\n0it [00:00, ?it/s]Extracting ../../data/Handwritten Num Recognition\\MNIST\\raw\\train-labels-idx1-ubyte.gz to ../../data/Handwritten Num Recognition\\MNIST\\raw\nDownloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../../data/Handwritten Num Recognition\\MNIST\\raw\\t10k-images-idx3-ubyte.gz\n1654784it [00:07, 207434.23it/s]\n0it [00:00, ?it/s]Extracting ../../data/Handwritten Num Recognition\\MNIST\\raw\\t10k-images-idx3-ubyte.gz to ../../data/Handwritten Num Recognition\\MNIST\\raw\nDownloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../../data/Handwritten Num Recognition\\MNIST\\raw\\t10k-labels-idx1-ubyte.gz\n8192it [00:04, 1710.63it/s]\nExtracting ../../data/Handwritten Num Recognition\\MNIST\\raw\\t10k-labels-idx1-ubyte.gz to ../../data/Handwritten Num Recognition\\MNIST\\raw\nProcessing...\nDone!\n"
    }
   ],
   "source": [
    "#定义预处理函数\n",
    "transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize([0.5], [0.5])])\n",
    "#下载数据，并对数据进行预处理\n",
    "train_dataset = mnist.MNIST('../../data/Handwritten Num Recognition', train=True, transform=transform, download=True)\n",
    "test_dataset = mnist.MNIST('../../data/Handwritten Num Recognition', train=False, transform=transform, download=True)\n",
    "#得到一个生成器\n",
    "train_loader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=test_batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "examples = enumerate(test_loader)\n",
    "batch_idx, (example_data, example_targets) = next(examples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "torch.Size([128, 1, 28, 28])"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "example_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2.3可视化源数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "examples = enumerate(test_loader)\n",
    "batch_idx, (example_data, example_targets) = next(examples)\n",
    "\n",
    "fig = plt.figure()\n",
    "for i in range(6):\n",
    "  plt.subplot(2,3,i+1)\n",
    "  plt.tight_layout()\n",
    "  plt.imshow(example_data[i][0], cmap='gray', interpolation='none')\n",
    "  plt.title(\"Ground Truth: {}\".format(example_targets[i]))\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2.4 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    \"\"\"\n",
    "    使用sequential构建网络，Sequential()函数的功能是将网络的层组合到一起\n",
    "    \"\"\"\n",
    "    def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim):\n",
    "        super(Net, self).__init__()\n",
    "        self.layer1 = nn.Sequential(nn.Linear(in_dim, n_hidden_1),nn.BatchNorm1d(n_hidden_1))\n",
    "        self.layer2 = nn.Sequential(nn.Linear(n_hidden_1, n_hidden_2),nn.BatchNorm1d(n_hidden_2))\n",
    "        self.layer3 = nn.Sequential(nn.Linear(n_hidden_2, out_dim))\n",
    "        \n",
    " \n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.layer1(x))\n",
    "        x = F.relu(self.layer2(x))\n",
    "        x = self.layer3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr = 0.01\n",
    "momentum = 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#实例化模型\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "#if torch.cuda.device_count() > 1:\n",
    "#    print(\"Let's use\", torch.cuda.device_count(), \"GPUs\")\n",
    "#    # dim = 0 [20, xxx] -> [10, ...], [10, ...] on 2GPUs\n",
    "#    model = nn.DataParallel(model)\n",
    "model = Net(28 * 28, 300, 100, 10)\n",
    "model.to(device)\n",
    "\n",
    "# 定义损失函数和优化器\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2.5 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0.009000000000000001\nepoch: 0, Train Loss: 0.2213, Train Acc: 0.9380, Test Loss: 0.0937, Test Acc: 0.9721\nepoch: 1, Train Loss: 0.0857, Train Acc: 0.9741, Test Loss: 0.0711, Test Acc: 0.9778\nepoch: 2, Train Loss: 0.0581, Train Acc: 0.9823, Test Loss: 0.0668, Test Acc: 0.9800\nepoch: 3, Train Loss: 0.0454, Train Acc: 0.9862, Test Loss: 0.0643, Test Acc: 0.9791\nepoch: 4, Train Loss: 0.0348, Train Acc: 0.9898, Test Loss: 0.0597, Test Acc: 0.9797\n0.008100000000000001\nepoch: 5, Train Loss: 0.0256, Train Acc: 0.9922, Test Loss: 0.0569, Test Acc: 0.9831\nepoch: 6, Train Loss: 0.0201, Train Acc: 0.9941, Test Loss: 0.0548, Test Acc: 0.9842\nepoch: 7, Train Loss: 0.0167, Train Acc: 0.9952, Test Loss: 0.0595, Test Acc: 0.9819\nepoch: 8, Train Loss: 0.0156, Train Acc: 0.9953, Test Loss: 0.0524, Test Acc: 0.9846\nepoch: 9, Train Loss: 0.0126, Train Acc: 0.9964, Test Loss: 0.0533, Test Acc: 0.9839\n0.007290000000000001\nepoch: 10, Train Loss: 0.0094, Train Acc: 0.9977, Test Loss: 0.0497, Test Acc: 0.9850\nepoch: 11, Train Loss: 0.0075, Train Acc: 0.9983, Test Loss: 0.0525, Test Acc: 0.9846\nepoch: 12, Train Loss: 0.0072, Train Acc: 0.9982, Test Loss: 0.0543, Test Acc: 0.9833\nepoch: 13, Train Loss: 0.0073, Train Acc: 0.9981, Test Loss: 0.0533, Test Acc: 0.9855\nepoch: 14, Train Loss: 0.0046, Train Acc: 0.9990, Test Loss: 0.0528, Test Acc: 0.9856\n0.006561000000000002\nepoch: 15, Train Loss: 0.0043, Train Acc: 0.9992, Test Loss: 0.0527, Test Acc: 0.9842\nepoch: 16, Train Loss: 0.0037, Train Acc: 0.9993, Test Loss: 0.0528, Test Acc: 0.9852\nepoch: 17, Train Loss: 0.0034, Train Acc: 0.9993, Test Loss: 0.0510, Test Acc: 0.9854\nepoch: 18, Train Loss: 0.0042, Train Acc: 0.9990, Test Loss: 0.0530, Test Acc: 0.9848\nepoch: 19, Train Loss: 0.0033, Train Acc: 0.9993, Test Loss: 0.0518, Test Acc: 0.9860\n"
    }
   ],
   "source": [
    "# 开始训练\n",
    "losses = [] #loss 数组\n",
    "acces = []  #acc  数组\n",
    "eval_losses = [] # 预测模式loss 数组\n",
    "eval_acces = []  # 预测模式acc 数组\n",
    "#writer = SummaryWriter(log_dir='logs',comment='train-loss')\n",
    "\n",
    "for epoch in range(num_epoches):\n",
    "    train_loss = 0\n",
    "    train_acc = 0\n",
    "    model.train()\n",
    "    #动态修改参数学习率\n",
    "    if epoch%5==0:\n",
    "        optimizer.param_groups[0]['lr']*=0.9\n",
    "        print(optimizer.param_groups[0]['lr'])\n",
    "    for img, label in train_loader:\n",
    "        img=img.to(device)\n",
    "        label = label.to(device)\n",
    "        img = img.view(img.size(0), -1)\n",
    "        # 前向传播\n",
    "        out = model(img)\n",
    "        loss = criterion(out, label)\n",
    "        # 反向传播\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        # 记录误差\n",
    "        train_loss += loss.item()\n",
    "        # 保存loss的数据与epoch数值\n",
    "        #writer.add_scalar('Train', train_loss/len(train_loader), epoch)\n",
    "        # 计算分类的准确率\n",
    "        _, pred = out.max(1)\n",
    "        num_correct = (pred == label).sum().item()\n",
    "        acc = num_correct / img.shape[0]\n",
    "        train_acc += acc\n",
    "        \n",
    "    losses.append(train_loss / len(train_loader))\n",
    "    acces.append(train_acc / len(train_loader))\n",
    "    # 在测试集上检验效果\n",
    "    eval_loss = 0\n",
    "    eval_acc = 0\n",
    "    #net.eval() # 将模型改为预测模式\n",
    "    model.eval()\n",
    "    for img, label in test_loader:\n",
    "        img=img.to(device)\n",
    "        label = label.to(device)\n",
    "        img = img.view(img.size(0), -1)\n",
    "        out = model(img)\n",
    "        loss = criterion(out, label)\n",
    "        # 记录误差\n",
    "        eval_loss += loss.item()\n",
    "        # 记录准确率\n",
    "        _, pred = out.max(1)\n",
    "        num_correct = (pred == label).sum().item()\n",
    "        acc = num_correct / img.shape[0]\n",
    "        eval_acc += acc\n",
    "        \n",
    "    eval_losses.append(eval_loss / len(test_loader))\n",
    "    eval_acces.append(eval_acc / len(test_loader))\n",
    "    print('epoch: {}, Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'\n",
    "          .format(epoch, train_loss / len(train_loader), train_acc / len(train_loader), \n",
    "                     eval_loss / len(test_loader), eval_acc / len(test_loader)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
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     "execution_count": 19
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     "output_type": "display_data",
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EgiC4QV+Yo6YbEQmE4AZ9UZRDR1uob2xJdykiIn0qsEHfcRVLtdOLiM8FNujLC+NBr+YbEfG74AZ9UfxLU9U6ISsiPhfYoC/IySAvO6Jvx4qI7wU26M2M8sKomm5ExPcCG/QQb77ZcVAnY0XE3wId9O03IHHOpbsUEZE+E+igLy+K0hRro6a+Kd2liIj0mWAHfaGuYiki/hfsoC9SX3oR8b9AB31Zxw1IdEJWRPwr0EGfnRGmJD9LfelFxNcCHfSA+tKLiO8p6IuiVKsvvYj4mIK+KMru2gaaY23pLkVEpE8o6AtzaHOw65CO6kXEnxT0RepLLyL+llTQm9lMM9tgZpvN7L4u5k83szfNLGZmH+80b7aZbfIes1NVeKroBiQi4ncnDXozCwMPAlcDE4CbzGxCp8W2A7cBv+60bhHwLWAqcCHwLTMr7H3ZqVOSn01G2NTzRkR8K5kj+guBzc65rc65ZmAhMCtxAefcNufc20DnM5ofAZY65w445w4CS4GZKag7ZcIho3RojppuRMS3kgn6UmBHwni1Ny0ZSa1rZneY2XIzW15TU5PkU6dOeVGUah3Ri4hPDYiTsc65R5xzVc65quLi4n5//fIifWlKRPwrmaDfCZQnjJd505LRm3X7TUVRlINHW6hvbEl3KSIiKZdM0L8BVJrZWDPLBG4EFiX5/EuAq8ys0DsJe5U3bUDpuFyxet6IiA+dNOidczHgbuIBvQ54wjm3xszmmdl1AGb2ATOrBm4AHjazNd66B4BvE99ZvAHM86YNKOVF3lUsdUJWRHwoksxCzrnFwOJO076ZMPwG8WaZrtZdACzoRY197lhfegW9iPjPgDgZm24FORnkZUUU9CLiSwp6wMwoK4qyQ1exFBEfUtB7KopydEQvIr6koPeUF0bZcfAozrl0lyIiklIKek95UZTGljZqDjeluxQRkZRS0Ht0FUsR8SsFvae9L/32A0fSXImISGop6D3lRVEKoxk8/uq7tLWpnV5E/ENB78mKhPn6Ryfw5vZD/PL1d9NdjohIyijoE1x/fikXVw7n+3/coHvIiohvKOgTmBnf+di5xNra+MYzq9XVUkR8QUHfScWwKF+acSbPrd/Hs6t2p7scEZFeU9B34TMfGsO5pQXcv2gNh442p7scEZFeUdB3IRIO8c/Xn8vBoy380+J16S5HRKRXFPTdmFhawP+4eBxPLK/mlc3vpbscEZFTpqA/gS9cWclpw6J85elVNLa0prscEZFToqA/geyMMP/8t+fy7v6jzH9uU7rLERE5JQr6k5h2+nBuuKCMR17ayppdtekuR0SkxxT0SfjaR8+mMJrBfU+tItbalu5yRER6REGfhKHRTO6/7hxW7azlsVe2pbscEZEeUdAn6aPnjuKKs0bwgz9t1J2oRGRQUdAnycz49scmEjL46tOrdHkEERk0FPQ9MHpoDnNnnsXLm97j6b/uTHc5IiJJUdD30Kc+eBrnVwzl279fy37ddlBEBgEFfQ+FQ8Z3/24Sh5tifPv3a9NdjojISSnoT8EZJXncdenpPLNyFy9s2JfuckRETkhBf4o+d9l4xhcP4etPr+ZIUyzd5YiIdEtBf4qyImG++3eT2HmogR/8aWO6yxER6ZaCvhc+MKaIT32wgsdeeYeVOw6luxwRkS4p6Htp7syzKM7L4r6n3qZFl0cQkQFIQd9L+dkZfHvWRNbvqeeRl7amuxwRkfdR0KfAVeeM5JpzRzL/uU2s31OX7nJERI6joE+R+//mHPKyIvztg6/wxBs7dIkEERkwFPQpMiI/m9/P+TCTy4cy96m3+dyv36T2aEu6yxIRUdCn0qiCHH7591P53zPP4k9r9nL1/Jd4fev+dJclIgGnoE+xcMi469LxPHXXNDIjIW589DX+75IN6pEjImmTVNCb2Uwz22Bmm83svi7mZ5nZb7z5r5vZGG/6GDNrMLOV3uOhFNc/YJ1XPpRn51zMDReU8ZMXNnPDQ6/y7v4j6S5LRALopEFvZmHgQeBqYAJwk5lN6LTY7cBB59zpwI+A7yXM2+Kcm+w97kxR3YPCkKwI3//4efzk5ilsqTnMNfNf5qkV1TpRKyL9Kpkj+guBzc65rc65ZmAhMKvTMrOAn3vDvwWuMDNLXZmD27WTRvPHL0znnNEFfOnJt7hn4UrqGnWiVkT6RzJBXwrsSBiv9qZ1uYxzLgbUAsO8eWPN7K9m9mczu7iX9Q5apUNz+Pc7PsiXZpzBs6t2c/W/vszybQfSXZaIBEBfn4zdDVQ456YA9wK/NrP8zguZ2R1mttzMltfU1PRxSekTDhmfv6KSJ++8iHDI+MTDr/KjpRuJ6UStiPShZIJ+J1CeMF7mTetyGTOLAAXAfudck3NuP4BzbgWwBTij8ws45x5xzlU556qKi4t7/lsMMudXFPLsnA/zscmlzH9uE5985DXdcFxE+kwyQf8GUGlmY80sE7gRWNRpmUXAbG/448DzzjlnZsXeyVzMbBxQCeiCMEBedgY//ORk5t84mY176rlm/sv8x0rdh1ZEUu+kQe+1ud8NLAHWAU8459aY2Twzu85b7N+AYWa2mXgTTXsXzOnA22a2kvhJ2judc2qYTjBrcimL77mYM0bmcc/Cldz5+Ap2HWpId1ki4iM20Lr6VVVVueXLl6e7jH4Xa23j4Ze28sDzmzCMOVdUcvuHx5IZ0XfaROTkzGyFc66qq3lKkQEiEg7xuctOZ9m9l3Bx5XC+98f1zJz/Ev+9+b10lyYig5yCfoApK4zyyK1V/Oy2D9Da5rjlp69z96/fZE9tY7pLE5FBSkE/QF121giWfGE6X7zyDJau3csVP3iRR1/aqmvmiEiPKegHsOyMMPdcWcnSL17C1HHD+M7idVwz/2Ve3aIrYopI8hT0g0DFsCgLbvsAP721ioaWVm569DW+sPCv7KtTc46InJyCfhC5ckIJS794CXMuP53Fq/ZwxQ/+zIL/ekffrBWRE1LQDzI5mWHuvepMlnxxOlNOK2Te79dy7QP/xRu6bo6IdENBP0iNHT6En3/mAzz0qfOpa2jhhode5UtPvKXmHBF5n0i6C5BTZ2bMnDiK6WcU85PnN/Poy1t5dtUuZl80hjsvGU/hkMx0lygiA4C+Gesj7+4/wr8u28QzK3cyJDPC7R8ey+0XjyU/OyPdpYlIHzvRN2MV9D60cW89P1q6kT+s3sPQaAb/c/p4Zk87jWimPsCJ+JWCPqBWVdfyg6UbeHFDDcNzs7j7svHcNLWCrEg43aWJSIop6ANu+bYD/MuSDbz+zgFGF2Qz54pK/u6CMjLCOhcv4he6qFnAVY0pYuEdH+SXt0+lOD+b+363ihk//DP/sXInrW0Da0cvIqmnoA8IM+PDlcN55n9N49Fbq+KXV1i4kqvnv8QfV+9hoH2yE5HUUdAHjJkxY0IJi+dczAM3TSHW6rjzlyu47if/zYsb9inwRXxIbfQBF2tt43d/3cn8ZZvYeaiBMcOizJhQwowJI7ngtELCIUt3iSKSBJ2MlZNqjrXx9F+reXbVHl7d8h4trY6iIZlcftYIZkwoYXplMTmZ6q0jMlAp6KVH6htb+PPGGpau3cvz6/dR3xgjKxLi4spirppQwuVnj2B4bla6yxSRBCcKen2DRt4nLzuDayeN5tpJo2lpbeMv7xxg6dq9LF27l2Xr9mIGF1QUek08JYwrzk13ySJyAjqil6Q551i7u64j9NfsqgNgfPEQZkwYyYwJJZxXVkBE/fNF+p2abqRP7DzUwDIv9F/bup9YmyM7I8SEUflMLC1g4ugCzinNp3JEHpkRhb9IX1LQS5+rbWjhpY01rNxxiNU7a1mzq47DTTEAMsMhzhyZx8TSYzuAM0fmkZ2hk7siqaKgl37X1ubYfuAoq3bWsnpXLWt21rF6Vy2HjrYAEA4ZlSNyveCP7wAmjM7XhddETpGCXgYE5xw7DzWwemctq73gX72zlvcONwNgBoXRTApyMsjPjpCfkxF/ZGfEp+VEEoYzjl8uO0PNQxJo6nUjA4KZUVYYpawwysyJo4B4+O+rb2JVdby5Z199I3WNMWobWqhraGHnwQbqGluobWihpfXEByU5GWHKi3KoHJFHZUkulSPyOKMklzHDh+gCbhJoCnpJKzOjJD+bkgnZXDmhpNvlnHM0trR1hH5dQ0vCcIy6hhYONbTw7v6jrN5Vy+LVu2n/sBoJGWOHD+kI/8qSXM4oyWPMsCH6FCCBoKCXQcHMyMkMk5MZpiQ/+6TLN7a0snnfYTbvO8zGvfVs2neYtbvq+MPqPcftAMYMH0LliFwqS/KoHJHLWSPzGDt8iLqIiq8o6MWXsjPC8RO9pQXHTW9saWVLTcIOYO9h1u+pZ8maPbRfsTkrEuKMkjzOHpXH2aPyOx4FObolowxOCnoJlOyMMOeMLuCc0e/fAWytOcL6PXWs213Hut31PLduH08sr+5YpnRoDmePymdCwg6goihKSBd+kwFOQS9CfAcwYXQ+E0bnd0xrP1G8dvex8F+3u47n1+/tOPofkhnmzJHHgn/MsCGMLMiiJD+bPN2UXQYIda8U6aHGllY27q3vCP/2HUF9Y+y45YZkhikpyGZkfnb8hHN+NiPzsxhZcGx8RF6WzgdISqh7pUgKZWeEmVQ2lEllQzumtX9HoPpgA3vrGtlT28ieukb21TWxp66Rv7xzgH31je/rImoGw3OzOnYGedkRwiEjEjIiYSMSCsXHw/Fp4VCIjJAR9sYjoRCRsBEOGRmh+A7D4Whz0OYczsVra0v42TG9i+WKhmQxvngI40fkMmxIJmZqlvIDBb1ICiR+R6A7bW2OA0eb2VPbGN8Z1DWyt66Jvd5OofrgUY40x2htdcTaHK1tjpbWNlrb4uPt0/pLQU5GPPSLcxlXnNuxA6goiup7CYOMgl6kn4RCxvDcLIbnZr2vN1CynHPHB3+rI9bWRszbKQCEzAiZYRb/xGAYISNhWny84yfHlq2pb2JLzRG27DvMlpr448WNNTy54thJ6UjIOG1YlPHFuYwfkRv/WTyEccW56pk0QCnoRQYRM68Zp4+uB9f+qeSSM4qPm17b0MLWmsNsrTnSsQPYUnOE59fvI5bwKSMzHDqu6SkcCpHR3rTU1bzQ8fMyIyGyIiGyIuFjwxnx8ayO8YThSNib760TPtbU1f5a4dCxJrCM8PHjkZC9r9dUS2sbR5tbaWhu5WhzLD7c0upNi48fm39smaPNrZhBblaEIVlhopkRbzhCblaYIR3D3s/MCNGscL98OlLQi8hJFeRkMKWikCkVhcdNb2ltY8eBo/FPATWHqW1oIdYa/4QR62iCausYjh03HJ/X0upoaGkl1tpGc6ujKdZKU0sbTbG2+HCsjeZYW5/9bmZ07BDizWU9ax7LjISIZoaJeldjPdwU40hza9LNbJmRUMfO4byyofzk5vN7/DucTFJBb2YzgflAGPipc+67neZnAb8ALgD2A590zm3z5n0FuB1oBeY455akrHoRSauMcIhxXhv+DLq/hEVvOedobvXCv+XYDiBxuLGlNWHnEt+ptCac22jfAR0/zdsReeNmxhDvG9jRzAjRjuH4Iycjcmw4M0xORrjLXlPOOZpibfHQb4p5P1s50hTjSHP7NG+8Y36M0UNz+mT7nTTozSwMPAjMAKqBN8xskXNubcJitwMHnXOnm9mNwPeAT5rZBOBG4BxgNLDMzM5wzrWm+hcREf8yM6/5JgwnvwJG2pkZ2RlhsjPCA+L+ysk0Dl0IbHbObXXONQMLgVmdlpkF/Nwb/i1whcX7Zc0CFjrnmpxz7wCbvecTEZF+kkzQlwI7EsarvWldLuOciwG1wLAk18XM7jCz5Wa2vKamJvnqRUTkpAZEZ1jn3CPOuSrnXFVxcfHJVxARkaQlE/Q7gfKE8TJvWpfLmFkEKCB+UjaZdUVEpA8lE/RvAJVmNtbMMomfXF3UaZlFwGxv+OPA8y5+EZ1FwI1mlmVmY4FK4C+pKV1ERJJx0l43zrmYmd0NLCHevXKBc26Nmc0DljvnFgH/BjxuZpuBA8R3BnjLPQGsBWLA59TjRkSkf+nqlSIiPnCiq1cOiJOxIiLSdwbcEb2Z1QDv9uIphgPvpaicvqD6ekf19Y7q652BXN9pzrkuuy0OuKDvLTNb3t3Hl4FA9fWO6usd1dc7A72+7qjpRkTE5xT0IiI+58egfyTdBZyE6usd1dc7qq93Bnp9XfJdG72IiBzPj0f0IiKSQEEvIuJzgzLozWymmW0ws81mdl8X87PM7Dfe/NfNbEw/1lZuZi+Y2VozW2Nm93SxzKVmVmtmK73HN/urvoQatpnZKu/13/dVZIv7sbcN3zaz1N/frPvazkzYNivNrM7MvtBpmX7dhma2wMz2mdnqhGlFZrbUzDZ5Pwu7WXe2t8wmM5vd1TJ9VN+/mNl67+/3tJkN7WbdE74X+rC++81sZ8Lf8Jpu1j3h/3sf1vebhNq2mdnKbtbt8+3Xa865QfUgfr2dLcA4IBN4C5jQaZn/BTzkDd8I/KYf6xsFnO8N5wEbu6jvUuD3ad6O24DhJ5h/DfAHwIAPAq+n8e+9h/iXQdK2DYHpwPnA6oRp3wfu84bvA77XxXpFwFbvZ6E3XNhP9V0FRLzh73VVXzLvhT6s737gy0n8/U/4/95X9XWa/wPgm+nafr19DMYj+t7c8arPOed2O+fe9IbrgXV0cbOVQWAW8AsX9xow1MxGpaGOK4AtzrnefFu615xzLxG/YF+ixPfZz4GPdbHqR4ClzrkDzrmDwFJgZn/U55z7k4vfCAjgNeKXCU+LbrZfMpL5f++1E9XnZccngH9P9ev2l8EY9L2541W/8pqMpgCvdzH7IjN7y8z+YGbn9G9lADjgT2a2wszu6GJ+UncH6wc30v0/WLq3YYlzbrc3vAe6vDv2QNmOnyX+Ca0rJ3sv9KW7vaalBd00fQ2E7XcxsNc5t6mb+encfkkZjEE/KJhZLvAU8AXnXF2n2W8Sb4o4D3gAeKafywP4sHPufOBq4HNmNj0NNZyQxe9/cB3wZBezB8I27ODin+EHZF9lM/sa8cuE/6qbRdL1Xvh/wHhgMrCbePPIQHQTJz6aH/D/S4Mx6Htzx6t+YWYZxEP+V86533We75yrc84d9oYXAxlmNry/6vNed6f3cx/wNO+/aftAuDvY1cCbzrm9nWcMhG0I7G1vzvJ+7utimbRuRzO7DbgWuMXbGb1PEu+FPuGc2+uca3XOtQGPdvO66d5+EeB64DfdLZOu7dcTgzHoe3PHqz7ntef9G7DOOffDbpYZ2X7OwMwuJP536M8d0RAzy2sfJn7SbnWnxRYBt3q9bz4I1CY0U/SXbo+k0r0NPYnvs9nAf3SxzBLgKjMr9JomrvKm9TkzmwnMBa5zzh3tZplk3gt9VV/iOZ+/7eZ1k/l/70tXAuudc9VdzUzn9uuRdJ8NPpUH8R4hG4mfjf+aN20e8Tc0QDbxj/ubid+6cFw/1vZh4h/h3wZWeo9rgDuBO71l7gbWEO9B8BowrZ+33zjvtd/y6mjfhok1GvCgt41XAVX9XOMQ4sFdkDAtbduQ+A5nN9BCvJ34duLnfZ4DNgHLgCJv2SrgpwnrftZ7L24GPtOP9W0m3r7d/j5s74k2Glh8ovdCP9X3uPfeept4eI/qXJ83/r7/9/6oz5v+WPt7LmHZft9+vX3oEggiIj43GJtuRESkBxT0IiI+p6AXEfE5Bb2IiM8p6EVEfE5BLyLicwp6ERGf+/+Uzl0uJ3q9SgAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.title('train loss')\n",
    "plt.plot(np.arange(len(losses)), losses)\n",
    "#plt.plot(np.arange(len(eval_losses)), eval_losses)\n",
    "#plt.legend(['Train Loss', 'Test Loss'], loc='upper right')\n",
    "plt.legend(['Train Loss'], loc='upper right')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将model保存为graph\n",
    "#writer.add_graph(model, (img,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Net1(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net4, self).__init__()\n",
    "        self.conv = torch.nn.Sequential(\n",
    "            OrderedDict(\n",
    "                [\n",
    "                    (\"conv1\", torch.nn.Conv2d(3, 32, 3, 1, 1)),\n",
    "                    (\"relu1\", torch.nn.ReLU()),\n",
    "                    (\"pool\", torch.nn.MaxPool2d(2))\n",
    "                ]\n",
    "            ))\n",
    "\n",
    "        self.dense = torch.nn.Sequential(\n",
    "            OrderedDict([\n",
    "                (\"dense1\", torch.nn.Linear(32 * 3 * 3, 128)),\n",
    "                (\"relu2\", torch.nn.ReLU()),\n",
    "                (\"dense2\", torch.nn.Linear(128, 10))\n",
    "            ])\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "['AdaptiveAvgPool1d', 'AdaptiveAvgPool2d', 'AdaptiveAvgPool3d', 'AdaptiveLogSoftmaxWithLoss', 'AdaptiveMaxPool1d', 'AdaptiveMaxPool2d', 'AdaptiveMaxPool3d', 'AlphaDropout', 'AvgPool1d', 'AvgPool2d', 'AvgPool3d', 'BCELoss', 'BCEWithLogitsLoss', 'BatchNorm1d', 'BatchNorm2d', 'BatchNorm3d', 'Bilinear', 'CELU', 'CTCLoss', 'ConstantPad1d', 'ConstantPad2d', 'ConstantPad3d', 'Container', 'Conv1d', 'Conv2d', 'Conv3d', 'ConvTranspose1d', 'ConvTranspose2d', 'ConvTranspose3d', 'CosineEmbeddingLoss', 'CosineSimilarity', 'CrossEntropyLoss', 'CrossMapLRN2d', 'DataParallel', 'Dropout', 'Dropout2d', 'Dropout3d', 'ELU', 'Embedding', 'EmbeddingBag', 'FeatureAlphaDropout', 'Flatten', 'Fold', 'FractionalMaxPool2d', 'FractionalMaxPool3d', 'GELU', 'GLU', 'GRU', 'GRUCell', 'GroupNorm', 'Hardshrink', 'Hardsigmoid', 'Hardswish', 'Hardtanh', 'HingeEmbeddingLoss', 'Identity', 'InstanceNorm1d', 'InstanceNorm2d', 'InstanceNorm3d', 'KLDivLoss', 'L1Loss', 'LPPool1d', 'LPPool2d', 'LSTM', 'LSTMCell', 'LayerNorm', 'LeakyReLU', 'Linear', 'LocalResponseNorm', 'LogSigmoid', 'LogSoftmax', 'MSELoss', 'MarginRankingLoss', 'MaxPool1d', 'MaxPool2d', 'MaxPool3d', 'MaxUnpool1d', 'MaxUnpool2d', 'MaxUnpool3d', 'Module', 'ModuleDict', 'ModuleList', 'MultiLabelMarginLoss', 'MultiLabelSoftMarginLoss', 'MultiMarginLoss', 'MultiheadAttention', 'NLLLoss', 'NLLLoss2d', 'PReLU', 'PairwiseDistance', 'Parameter', 'ParameterDict', 'ParameterList', 'PixelShuffle', 'PoissonNLLLoss', 'RNN', 'RNNBase', 'RNNCell', 'RNNCellBase', 'RReLU', 'ReLU', 'ReLU6', 'ReflectionPad1d', 'ReflectionPad2d', 'ReplicationPad1d', 'ReplicationPad2d', 'ReplicationPad3d', 'SELU', 'Sequential', 'Sigmoid', 'SmoothL1Loss', 'SoftMarginLoss', 'Softmax', 'Softmax2d', 'Softmin', 'Softplus', 'Softshrink', 'Softsign', 'SyncBatchNorm', 'Tanh', 'Tanhshrink', 'Threshold', 'Transformer', 'TransformerDecoder', 'TransformerDecoderLayer', 'TransformerEncoder', 'TransformerEncoderLayer', 'TripletMarginLoss', 'Unfold', 'Upsample', 'UpsamplingBilinear2d', 'UpsamplingNearest2d', 'ZeroPad2d', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '_reduction', 'common_types', 'functional', 'grad', 'init', 'intrinsic', 'modules', 'parallel', 'parameter', 'qat', 'quantized', 'utils']\n"
    }
   ],
   "source": [
    "print(dir(torch.nn))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.6 优化器</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.utils.data as Data\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    " \n",
    "# 超参数\n",
    "LR = 0.01\n",
    "BATCH_SIZE = 32\n",
    "EPOCH = 12\n",
    " \n",
    "# 生成训练数据\n",
    "# torch.unsqueeze() 的作用是将一维变二维，torch只能处理二维的数据\n",
    "x = torch.unsqueeze(torch.linspace(-1, 1, 1000), dim=1)  \n",
    "# 0.1 * torch.normal(x.size())增加噪点\n",
    "y = x.pow(2) + 0.1 * torch.normal(torch.zeros(*x.size()))\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "torch_dataset = Data.TensorDataset(x,y)\n",
    "#得到一个代批量的生成器\n",
    "loader = Data.DataLoader(dataset=torch_dataset, batch_size=BATCH_SIZE, shuffle=True)\n",
    " \n",
    " \n",
    "class Net2(torch.nn.Module):\n",
    "    # 初始化\n",
    "    def __init__(self):\n",
    "        super(Net2, self).__init__()\n",
    "        self.hidden = torch.nn.Linear(1, 20)\n",
    "        self.predict = torch.nn.Linear(20, 1)\n",
    " \n",
    "    # 前向传递\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.hidden(x))\n",
    "        x = self.predict(x)\n",
    "        return x\n",
    " \n",
    "net_SGD = Net2()\n",
    "net_Momentum = Net2()\n",
    "net_RMSProp = Net2()\n",
    "net_Adam = Net2()\n",
    " \n",
    "nets = [net_SGD, net_Momentum, net_RMSProp, net_Adam]\n",
    " \n",
    "opt_SGD = torch.optim.SGD(net_SGD.parameters(), lr=LR)\n",
    "opt_Momentum = torch.optim.SGD(net_Momentum.parameters(), lr=LR, momentum=0.9)\n",
    "opt_RMSProp = torch.optim.RMSprop(net_RMSProp.parameters(), lr=LR, alpha=0.9)\n",
    "opt_Adam = torch.optim.Adam(net_Adam.parameters(), lr=LR, betas=(0.9, 0.99))\n",
    "optimizers = [opt_SGD, opt_Momentum, opt_RMSProp, opt_Adam]\n",
    " \n",
    "loss_func = torch.nn.MSELoss()\n",
    " \n",
    "loss_his = [[], [], [], []]  # 记录损失\n",
    " \n",
    "for epoch in range(EPOCH):\n",
    "    for step, (batch_x, batch_y) in enumerate(loader):\n",
    "        for net, opt,l_his in zip(nets, optimizers, loss_his):\n",
    "            output = net(batch_x)  # get output for every net\n",
    "            loss = loss_func(output, batch_y)  # compute loss for every net\n",
    "            opt.zero_grad()  # clear gradients for next train\n",
    "            loss.backward()  # backpropagation, compute gradients\n",
    "            opt.step()  # apply gradients\n",
    "            l_his.append(loss.data.numpy())  # loss recoder\n",
    "labels = ['SGD', 'Momentum', 'RMSprop', 'Adam']\n",
    "for i, l_his in enumerate(loss_his):\n",
    "    plt.plot(l_his, label=labels[i])\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('Steps')\n",
    "plt.ylabel('Loss')\n",
    "plt.ylim((0, 0.2))\n",
    "plt.show()\n"
   ]
  }
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